Survey report: data management in Citizen Science projects

A Citizen Science and Smart City Summit, organised by the European Commission’s Joint Research Centre (JRC) in 2014, identified the management of citizen-collected data as a major barrier to the re-usability and integration of these contributions across borders. We followed up on these find-ings with a Citizen Science survey, experiments on a repository for EU-funded Citizen Science projects, and discussions with the European and international Citizen Science community. This report summarises the outcomes of the survey. Amongst other findings, the 121 responses clearly underlined the diversity of projects in terms of topicality, funding mechanisms and geographic coverage, but also provided valuable insights relat-ed to the access and re-use conditions of project results. While, for example, 60% of the participat-ing projects follow a dedicated data management plan and a majority of projects provides access to raw or aggregated data, the exact use conditions are not always put into place or miss well-defined licenses. Apart from replies from all across the globe, this activity also helped us to connect to the relevant players. Discussions on data management in support of Citizen Science could already be initiated with representatives of the European, American and Australian Citizen Science associations.JRC.H.6-Digital Earth and Reference Dat

Historical Evolution of Artificial Intelligence: Analysis of the three main paradigm shifts in AI

Artificial intelligence (AI) can have a major impact on the way modern societies respond to the hard challenges they face. Properly harnessed, AI can create a more fair, healthy, and inclusive society. Today, AI has become a mature technology and an increasingly important part of the modern life fabric. AI is already deployed in different application domains, e.g. recommendation systems, spam filters, image recognition, voice recognition, virtual assistants, etc. It spans across many sectors, from medicine to transportation, and across decades, since the term was introduced in the 1950s. The approaches also evolved, from the foundational AI algorithms of the 1950s, to the paradigm shift in symbolic algorithms and expert system development in the 1970s, the introduction of machine learning in the 1990s and the deep learning algorithms of the 2010s. Starting with the fundamental definitions and building on the historical context, this report summarizes the evolution of AI, it introduces the “seasons” of AI development (i.e. winters for the decline and springs for the growth), describes the current rise of interest in AI, and concludes with the uncertainty on the future of AI, with chances of another AI winter or of an even greater AI spring.JRC.B.6-Digital Econom

Using new data sources for policymaking

This JRC technical report synthesises the results of our work on using new data sources for policy-making. It reflects a recent shift from more general considerations in the area of Big Data to a more dedicated investigation of Citizen Science, and it summarizes the state of play. With this contribution, we start promoting Citizen Science as an integral component of public participation in policy in Europe. The particular need to focus on the citizen dimension emerged due to (i) the increasing interest in the topic from policy Directorate-Generals (DGs) of the European Commission (EC), (ii) the considerable socio-economic impact policy making has on citizens’ life and society as a whole, and (iii) the clear potentiality of citizens’ contributions to increase the relevance of policy making and the effectiveness of policies when addressing societal challenges. We explicitly concentrate on Citizen Science (or public participation in scientific research) as a way to engage people in practical work, and to develop a mutual understanding between the participants from civil society, research institutions and the public sector by working together on a topic that is of common interest.JRC.B.6-Digital Econom

AI Watch 2019 Activity Report

This report provides an overview of AI Watch activities in 2019. AI Watch is the European Commission knowledge service to monitor the development, uptake and impact of Artificial Intelligence (AI) for Europe.,. As part of the European strategy on AI, the European Commission and the Member States published in December 2018 a “Coordinated Plan on Artificial Intelligence” on the development of AI in the EU. The Coordinated Plan mentions the role of AI Watch to monitor its implementation. AI Watch was launched in December 2018. It aims to monitor European Union’s industrial, technological and research capacity in AI; AI national strategies and policy initiatives in the EU Member States; uptake and technical developments of AI; and AI use and impact in public services. AI Watch will also provide analyses of education and skills for AI; AI key technological enablers; data ecosystems; and social perspective on AI. AI Watch has a European focus within the global landscape, and works in coordination with Member States. In its first year AI Watch has developed and proposed methodologies for data collection and analysis in a wide scope of AI-impacted domains, and has presented new results that can already support policy making on AI in the EU. In the coming months AI Watch will continue collecting and analysing new information. All AI Watch results and analyses are published on the AI Watch public web portal (https://ec.europa.eu/knowledge4policy/ai-watch_en). AI Watch welcomes feedback. This report will be updated annually.JRC.B.6-Digital Econom

Citizen Science

The Citizen Science collection contains data sets relating to citizen engagement in scientific research in the widest sense. It includes the results of a survey of citizen science projects.JRC.B.6-Digital Econom

Scientific data from and for the citizen

Powered by advances of technology, today’s Citizen Science projects cover a wide range of thematic areas and are carried out from local to global levels. This wealth of activities creates an abundance of data, for example, in the forms of observations submitted by mobile phones; readings of low-cost sensors; or more general information about peoples’ activities. The management and possible sharing of this data has become a research topic in its own right. We conducted a survey in the summer of 2015 in order to collectively analyze the state of play in Citizen Science. This paper summarizes our main findings related to data access, standardization and data preservation. We provide examples of good practices in each of these areas and outline actions to address identified challenges.JRC.B.6-Digital Econom

Historical Evolution of Artificial Intelligence

Artificial intelligence (AI) can have a major impact on the way modern societies respond to the hard challenges they face. Properly harnessed, AI can create a more fair, healthy, and inclusive society. Today, AI has become a mature technology and an increasingly important part of the modern life fabric. AI is already deployed in different application domains, e.g. recommendation systems, spam filters, image recognition, voice recognition, virtual assistants, etc.It spans across many sectors, from medicine to transportation, and across decades, since the term was introduced in the 1950s. The approaches also evolved, from the foundational AI algorithms of the 1950s, to the paradigm shift in symbolic algorithms and expert system development in the 1970s, the introduction of machine learning in the 1990s and the deep learning algorithms of the 2010s.Starting with the fundamental definitions and building on the historical context, this report summarizes the evolution of AI, it introduces the “seasons” of AI development (i.e. winters for the decline and springs for the growth), describes the current rise of interest in AI, and concludes with the uncertainty on the future of AI, with chances of another AI winter or of an even greater AI spring

Using Foursquare place data for estimating building block use

Information about the Land Use (LU) of built-up areas is required for the comprehensive planning and management of cities. However, due to the high cost of the LU surveys, LU data is out-dated or not available for many cities. Therefore, we propose the reuse of up-to-date and low-cost place data from social media applications for LU mapping purposes. As main case study, we used Foursquare place data for estimating non-residential Building Block Use (BBU) in the city of Amsterdam. Based on the Foursquare place categories, we estimated the use of 9,827 building blocks, and we compared the classification results with a reference BBU dataset. Our evaluation metric is the kappa coefficient, which determines if the classification results are significantly better than a random guess result. Using the optimal set of parameter values, we achieved the highest kappa coefficient values for the LU categories “hotels, restaurants & cafes” (0.76) and “retail” (0.65). The lowest kappa coefficients were found for the LU categories “industries” and “storage & unclear”. We have also applied the methodology in another case study area, the city of Varese in Italy, where we had similar accuracy results. We therefore conclude that Foursquare place data can be trusted only for the estimation of particular LU categories.JRC.H.6-Digital Earth and Reference Dat

The JRC Multidisciplinary Research Data Infrastructure

This paper presents the approach adopted by the European Commission’s Joint Research Centre (JRC) in order to facilitate open access to its research data crated as support for EU policies, which is also in line with the general Open Data trend. The paper presents various initiatives and incentives that are put in place at the JRC in order to progressively implement a multi-disciplinary research data infrastructure for the fulfilment of the corporate data policy goals. These include, among others, the JRC metadata schema that is developed with the aim to harmonise the way data are described, as well as the architecture of a data infrastructure designed to support the multidisciplinary nature of the JRC activities.JRC.B.6-Digital Econom

Mobile multimedia event capturing and visualization (MOME)

Summarization: Capturing Multimedia Events such as natural disasters, accident reports, building damage reports, political events, etc., are expensive functionalities due to the number and training of the people required, as well as the time involved in the capturing and post-processing of multimedia. In addition, the captured multimedia content often fails to give the viewer a comprehensive understanding of the event captured in context. We present a model and a mobile system for multimedia event capturing by a one-man-crew. The system supports: (a) the real time capturing of complex multimedia events of different types, (b) the recording of the capturing process and the metadata associated with the events, (c) the visualization of the events and the capturing process, and (d) the learning and preparation of the one-man-crew that will do the multimedia event capturing.Presented on